1. Field of the Invention
The present invention relates to process control systems including computer-based materials management. More precisely, the present invention relates to a feed forward process control system used in semiconductor fabrication based on material groupings.
2. Description of the Related Art
In the past, semiconductor manufacturing process control was largely achieved by ensuring that process parameters were set on a machine controller according to machine-dependent recipes. The basic philosophy of conventional semiconductor manufacturing process control is that if all settings that affect the process are set correctly, the machine will consistently produce a specified product. Using the conventional approach, manufacturing personnel act to relate machine settings to product characteristics. The approach has yet to be fully realized, however, due to a number of factors, including variability in equipment performance, variability in incoming materials such as wafers and chemicals, increasingly complex processes, and a lack of adequate models relating process settings to product characteristics. Success using the conventional process control approach becomes much less likely as the size of wafer features becomes smaller.
Engineers have derived recipe settings based largely on experience, intuition, and, more recently, Response Surface Methodology (RSM) experiments. Initially, the recipes were manually downloaded to the equipment by operators/technicians. Subsequently, Factory Control Systems incorporating Equipment Integration (EI) functionality provided automated recipe management and download operations.
Most recently, engineers have used Statistical Process Controls (SPC) concepts and methods for monitoring the performance of processes to verify that a process remains in a state of "statistical control." Initially, operators and technicians performed SPC manually. Subsequently, all-manual charting was replaced with computerized factory control systems (FCS)-based SPC charts. In some cases automated Trouble Shooting Guides (TSGs) supplied automation to process control tasks. SPC is a fault detection methodology. TSGs perform rule-based classification and assist with problem resolution. SPC helps distinguish between two types of process variation: common and special. SPC out-of-control signals are clues that are useful for identifying sources of special variation. Once a cause for special variation is determined, manufacturing can produce improvements in the process and product quality. As a fault detection and classification methodology, SPC relies upon an intimate understanding of the process and is largely manual and reactive. FIG. 1 is a schematic block diagram that shows a traditional SPC process.
The conventional process control approaches have resulted in substantial progress. However, reactive process control techniques such as SPC do not achieve and sustain desired product yields and resource productivity, particularly in light of the size and speed specifications of future products. The semiconductor industry must continue to develop and deploy new process control methods. To address significant unresolved problems, an Advanced Process Control (APC) Framework is needed with the ability to provide:
(1) Sensor-based automated fault detection to provide in real time the equipment or process conditions that result in a misprocessed wafer. PA1 (2) Classification of detected faults to determine the cause of a fault and expedite repair of a tool. PA1 (3) Model-based run-to-run process control using sensor inputs, process models, and process control strategies to ensure that the process remains optimal for every die on every wafer. PA1 (4) Model-based real-time process control using in situ inputs, process models, and process control strategies to correctly process control parameters during the process run, ensuring that product characteristics are achieved.
Common Object Request Broker Architecture (CORBA) based technologies are used as a communication interface between clients and servers, often in highly complex systems. Due to the complex nature of the systems, testing can be difficult. The system complexity arises because multiple components interact with one another over a network, which introduces problems. Many components operate both as a client and as a server since, in servicing a request, a component calls other remote services which, in turn, call other remote services. Testing of a client-server component is difficult since the component uses a driver to send requests and to send harnesses to emulate other components that interact with the client-server component. Also failures and performance problems may occur in any of multiple potentially remote components and therefore be difficult to isolate.
The problems and complexities of these technologies including complexities arising from the integration of multiple components, the verification of the correct operation of all of the multiple components individually and while interacting, and the analysis not only of correct operation but also of performance place a high demand on system test personnel. No longer can the least experienced developers perform the testing. More experienced architects are needed to specify and set up a testing infrastructure and perform the tests.
What is needed is a strategy and technique that improves the management and prospects for success of the testing process.
Present-day semiconductor manufacturing environments include the following characteristics that limit the ability of an environment to support the manufacture of complex, high-value products. First, stand-alone equipment controllers have limited communications capability and limited provisions for external process control. Second, semiconductor manufacturing environments are limited by "static" (nonadaptive) process control approaches. Furthermore, present-day semiconductor manufacturing environments lack models to support the development and use of control algorithms. In addition the manufacturing environments are supplied by nonuniform, disparate, and incomplete sources of manufacturing data for driving process control algorithms. Closed, monolithic factory system architectures prevent integration of new capabilities, especially from multiple suppliers.